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A curated, but probably biased and incomplete, list of awesome Fairness in AI resources.

If you want to contribute to this list, feel free to pull a request. Also you can contact Mengnan Du from the Data Lab at Texas A&M University through email: dumengnan@tamu.edu, or Twitter @DuMNCH.

What is Fairness in AI?

AI algorithms are increasingly being used in high-stake decision making applications that affect individual lives. However, AI might exhibit algorithmic discrimination behaviors with respect to protected groups, potentially posing negative impacts on individuals and society.

Fairness in AI (FAI) aims to build fair and unbiased AI/machine learning systems, that ensure the benefits are broadly available across all segments of society. Specific topics include but are not limited to: theoretical understanding of algorithmic bias, defining measurements of fairness, detection of adverse biases, and developing mitigation strategies.

Table of Contents

Review and General Papers

Measurements of Fairness

Demonstration of Bias Phemomenon in Various Applications

Bias in Machine Learning Models

Bias in Representations

Mitigation of Unfairness

Mitigation of Machine Learning Models

Mitigation of Representations

Fairness Packages and Frameworks

Conferences

Other Fairness Relevant Interpretability Resources